Ali Mostafa Z, Awad Noor H, Suganthan Ponnuthurai Nagaratnam, Reynolds Robert G
IEEE Trans Cybern. 2017 Sep;47(9):2768-2779. doi: 10.1109/TCYB.2016.2617301. Epub 2016 Oct 25.
Developing efficient evolutionary algorithms attracts many researchers due to the existence of optimization problems in numerous real-world applications. A new differential evolution algorithm, sTDE-dR, is proposed to improve the search quality, avoid premature convergence, and stagnation. The population is clustered in multiple tribes and utilizes an ensemble of different mutation and crossover strategies. In this algorithm, a competitive success-based scheme is introduced to determine the life cycle of each tribe and its participation ratio for the next generation. In each tribe, a different adaptive scheme is used to control the scaling factor and crossover rate. The mean success of each subgroup is used to calculate the ratio of its participation for the next generation. This guarantees that successful tribes with the best adaptive schemes are only the ones that guide the search toward the optimal solution. The population size is dynamically reduced using a dynamic reduction method. Comprehensive comparison of the proposed heuristic over a challenging set of benchmarks from the CEC2014 real parameter single objective competition against several state-of-the-art algorithms is performed. The results affirm robustness of the proposed approach compared to other state-of-the-art algorithms.
由于众多实际应用中存在优化问题，开发高效的进化算法吸引了许多研究人员。提出了一种新的差分进化算法sTDE-dR，以提高搜索质量，避免过早收敛和停滞。种群被聚类为多个部落，并采用不同变异和交叉策略的集合。在该算法中，引入了一种基于竞争成功的方案来确定每个部落的生命周期及其下一代的参与率。在每个部落中，使用不同的自适应方案来控制缩放因子和交叉率。每个子群体的平均成功率用于计算其下一代的参与率。这保证了具有最佳自适应方案的成功部落才是引导搜索朝着最优解方向进行的部落。使用动态缩减方法动态减小种群规模。针对CEC2014实参数单目标竞赛中一组具有挑战性的基准测试，将所提出的启发式算法与几种现有最先进算法进行了全面比较。结果证实了所提出方法相对于其他现有最先进算法的鲁棒性。
